Preprocess data.frame for generating a nowcast — preprocess_for

Function that takes a data frame with `onset_date` and `report_date` and generates all possible combinations of onset_dates and report_dates observable controlling by the covariates specified in `...`

Usage

preprocess_for_nowcast(
  .disease_data,
  onset_date,
  report_date,
  strata = NULL,
  now,
  units,
  max_delay = Inf,
  data_type = c("auto", "linelist", "count")
)

Arguments

.disease_data: A time series of reporting data in aggregated line list format such that each row has a column for onset date, report date, and
onset_date: In quotations, the name of the column of datatype Date designating the date of case onset. e.g. "onset_week"
report_date: In quotations, the name of the column of datatype Date designating the date of case report. e.g. "report_week"
strata: Character vector of names of the strata included in the data.
now: An object of datatype Date indicating the date at which to perform the nowcast.
units: Time scale of reporting. Options: "1 day", "1 week".
max_delay: Maximum possible delay observed or considered for estimation of the delay distribution (numeric). Default: `Inf`
data_type: Either `linedata` if each row represents a test or `counts` if there is a column named `n` with counts of how many tests had that onset and report dates

Value

A `data.frame` with all possible counts for all delay-onset combinations. The new column with the counts is named `n`. Additional columns `.tval` and `.delay` are added where `.tval` codifies the dates as numbers (starting at 0) and delay codifies the difference between onset and report.

Examples

data(denguedat)

# Get counts by onset date and report week consider all possible delays
preprocess_for_nowcast(denguedat, "onset_week", "report_week",
  units = "weeks", now = as.Date("1990-03-05")
)
#> ℹ Assuming data is linelist-data where each observation is a test. If you are working with count-data set `data_type = "count"`
#> # A tibble: 55 × 5
#>    onset_week .delay report_week     n .tval
#>    <date>      <dbl> <date>      <int> <dbl>
#>  1 1990-01-01      0 1990-01-01      3     1
#>  2 1990-01-01      1 1990-01-08     24     1
#>  3 1990-01-01      2 1990-01-15     23     1
#>  4 1990-01-01      3 1990-01-22      8     1
#>  5 1990-01-01      4 1990-01-29      1     1
#>  6 1990-01-01      5 1990-02-05      0     1
#>  7 1990-01-01      6 1990-02-12      1     1
#>  8 1990-01-01      7 1990-02-19      0     1
#>  9 1990-01-01      8 1990-02-26      0     1
#> 10 1990-01-01      9 1990-03-05      1     1
#> # ℹ 45 more rows

# Complete one date when there was no onset week
df <- data.frame(
  onset_week  = as.Date(c("1994-09-19", "1994-10-03", "1994-10-03", "1994-10-10")),
  report_week = as.Date(c("1994-09-19", "1994-10-03", "1994-10-10", "1994-10-10"))
)
preprocess_for_nowcast(df, "onset_week", "report_week",
  units = "weeks",
  now = as.Date("1994-10-10")
)
#> ℹ Assuming data is linelist-data where each observation is a test. If you are working with count-data set `data_type = "count"`
#> # A tibble: 7 × 5
#>   onset_week .delay report_week     n .tval
#>   <date>      <dbl> <date>      <int> <dbl>
#> 1 1994-09-19      0 1994-09-19      1     1
#> 2 1994-09-19      1 1994-09-26      0     1
#> 3 1994-09-26      0 1994-09-26      0     2
#> 4 1994-09-26      1 1994-10-03      0     2
#> 5 1994-10-03      0 1994-10-03      1     3
#> 6 1994-10-03      1 1994-10-10      1     3
#> 7 1994-10-10      0 1994-10-10      1     4

# Complete one date when there was no report of delay 3 mostly
df <- data.frame(
  onset_week  = as.Date(c("1994-09-19", "1994-10-03", "1994-10-03", "1994-10-10")),
  report_week = as.Date(c("1994-10-10", "1994-10-03", "1994-10-10", "1994-10-10"))
)
preprocess_for_nowcast(df, "onset_week", "report_week",
  units = "weeks",
  now = as.Date("1994-10-10")
)
#> ℹ Assuming data is linelist-data where each observation is a test. If you are working with count-data set `data_type = "count"`
#> # A tibble: 10 × 5
#>    onset_week .delay report_week     n .tval
#>    <date>      <dbl> <date>      <int> <dbl>
#>  1 1994-09-19      0 1994-09-19      0     1
#>  2 1994-09-19      1 1994-09-26      0     1
#>  3 1994-09-19      2 1994-10-03      0     1
#>  4 1994-09-19      3 1994-10-10      1     1
#>  5 1994-09-26      0 1994-09-26      0     2
#>  6 1994-09-26      1 1994-10-03      0     2
#>  7 1994-09-26      2 1994-10-10      0     2
#>  8 1994-10-03      0 1994-10-03      1     3
#>  9 1994-10-03      1 1994-10-10      1     3
#> 10 1994-10-10      0 1994-10-10      1     4

# Get counts by onset date and report week stratifying by gender and state
df <- data.frame(
  onset_week = sample(as.Date(c("1994-09-19", "1994-10-03", "1994-10-10")), 100, replace = TRUE),
  gender = sample(c("Male", "Female"), 100, replace = TRUE),
  state = sample(c("A", "B", "C", "D"), prob = c(0.5, 0.2, 0.2, 0.1), size = 100, replace = TRUE)
)
df$report_week <- df$onset_week +
  sample(c(lubridate::weeks(1), lubridate::weeks(2)), 100, replace = TRUE)
preprocess_for_nowcast(df, "onset_week", "report_week", c("gender", "state"),
  units = "weeks",
  now = as.Date("1994-09-26")
)
#> ℹ Assuming data is linelist-data where each observation is a test. If you are working with count-data set `data_type = "count"`
#> # A tibble: 8 × 7
#>   onset_week .delay gender state report_week     n .tval
#>   <date>      <dbl> <chr>  <chr> <date>      <int> <dbl>
#> 1 1994-09-19      1 Male   A     1994-09-26      2     1
#> 2 1994-09-19      1 Male   D     1994-09-26      2     1
#> 3 1994-09-19      1 Male   B     1994-09-26      0     1
#> 4 1994-09-19      1 Male   C     1994-09-26      2     1
#> 5 1994-09-19      1 Female A     1994-09-26      2     1
#> 6 1994-09-19      1 Female D     1994-09-26      2     1
#> 7 1994-09-19      1 Female B     1994-09-26      3     1
#> 8 1994-09-19      1 Female C     1994-09-26      5     1